Abstract
Improving scheduling methods in manufacturing environments such as job shops offers the potential to increase throughput, decrease costs, and therefore increase profit. This makes scheduling an important aspect in the manufacturing industry. Job shop scheduling has been widely studied in the academic literature because of its real-world applicability and difficult nature. Dispatching rules are the most common means of scheduling in dynamic environments. We use genetic programming to search the space of potential dispatching rules. Dispatching rules are often short-sighted as they make one instantaneous decision at each decision point. We incorporate local search into the evaluation of dispatching rules to assess the quality of decisions made by dispatching rules and encourage the dispatching rules to make good local decisions for effective overall performance. Results show that the inclusion of local search in evaluation led to the evolution of dispatching rules which make better decisions over the local time horizon, and attain lower total weighted tardiness. The advantages of using local search as a tie-breaking mechanism are not so pronounced.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Aarts, E., Lenstra, J. (eds.): Local Search in Combinatorial Optimization. Wiley, Chichester (1997)
Baker, K.R.: Sequencing rules and due-date assignments in a job shop. Manage. Sci. 30(9), 1093–1104 (1984)
Blazewicz, J., Domschke, W., Pesch, E.: The job shop scheduling problem: conventional and new solution techniques. Eur. J. Oper. Res. 93(1), 133 (1996)
Burke, E.K., Hyde, M.R., Kendall, G., Ochoa, G., Ozcan, E., Woodward, J.R.: Exploring hyper-heuristic methodologies with genetic programming. In: Mumford, C., Jain, L. (eds.) Computational Intelligence, Intelligent Systems Reference Library, vol. 1, pp. 177–201. Springer, Heidelberg (2009)
Deb, K., Pratap, A., Agarwal, S., Meyarivan, T.: A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans. Evol. Comput. 6(2), 182–197 (2002)
Geiger, C., Uzsoy, R., Aytug, H.: Rapid modeling and discovery of priority dispatching rules: an autonomous learning approach. J. Sched. 9(1), 734 (2006)
Hart, E., Ross, P., Corne, D.: Evolutionary scheduling: a review. Genet. Program. Evolvable Mach. 6, 191–220 (2005)
Hildebrandt, T., Heger, J., Scholz-Reiter, B.: Towards improved dispatching rules for complex shop floor scenarios: a genetic programming approach. In: Proceedings of the 12th Annual Conference on Genetic and Evolutionary Computation, pp. 257–264 (2010)
Hunt, R., Johnston, M., Zhang, M.: Evolving machine-specific dispatching rules for a two-machine job shop using genetic programming. In: Proceedings of the IEEE Congress on Evolutionary Computation, pp. 618–625 (2014)
Hunt, R., Johnston, M., Zhang, M.: Evolving less-myopic scheduling rules for dynamic job shop scheduling with genetic programming. In: Proceedings of the Genetic and Evolutionary Computation Conference, pp. 927–934 (2014)
Jakobović, D., Jelenković, L., Budin, L.: Genetic programming heuristics for multiple machine scheduling. In: Ebner, M., O’Neill, M., Ekárt, A., Vanneschi, L., Esparcia-Alcázar, A.I. (eds.) EuroGP 2007. LNCS, vol. 4445, pp. 321–330. Springer, Heidelberg (2007)
Jakobovi, D., Marasovi, K.: Evolving priority scheduling heuristics with genetic programming. Appl. Soft Comput. 12(9), 2781–2789 (2012)
Jones, A., Rabelo, L.C.: Survey of Job Shop Scheduling Techniques. Technical report, National Institute of Standards and Technology, Gaithersberg (1998)
Koza, J.R.: Genetic Programming: On the Programming of Computers by Means of Natural Selection. MIT Press, Cambridge (1992)
Luke, S.: Essentials of Metaheuristics, 2nd edn. Lulu (2013). http://cs.gmu.edu/~sean/book/metaheuristics/
Nguyen, S., Zhang, M., Johnston, M., Tan, K.C.: A coevolution genetic programming method to evolve scheduling policies for dynamic multi-objective job shop scheduling problems. In: Proceedings of the IEEE Congress on Evolutionary Computation, pp. 3261–3268 (2012)
Nguyen, S., Zhang, M., Johnston, M., Tan, K.C.: A computational study of representations in genetic programming to evolve dispatching rules for the job shop scheduling problem. IEEE Trans. Evol. Comput. 17(5), 621–639 (2013)
Pickardt, C.W., Hildebrandt, T., Branke, J., Heger, J., Scholz-Reiter, B.: Evolutionary generation of dispatching rule sets for complex dynamic scheduling problems. Int. J. Prod. Econ. 145(1), 67–77 (2013)
Pinedo, M.L.: Scheduling: Theory, Algorithms, and Systems, 3rd edn. Springer, Heidelberg (2008)
Potts, C., Strusevich, V.: Fifty years of scheduling: a survey of milestones. J. Oper. Res. Soc. 60(S1), 41–68 (2009)
Vepsalainen, A., Morton, T.: Priority rules for job shops with weighted tardiness costs. Manage. Sci. 33, 1035–1047 (1987)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Hunt, R., Johnston, M., Zhang, M. (2015). Using Local Search to Evaluate Dispatching Rules in Dynamic Job Shop Scheduling. In: Ochoa, G., Chicano, F. (eds) Evolutionary Computation in Combinatorial Optimization. EvoCOP 2015. Lecture Notes in Computer Science(), vol 9026. Springer, Cham. https://doi.org/10.1007/978-3-319-16468-7_19
Download citation
DOI: https://doi.org/10.1007/978-3-319-16468-7_19
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-16467-0
Online ISBN: 978-3-319-16468-7
eBook Packages: Computer ScienceComputer Science (R0)